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Polygon decomposition based on the straight line skeleton
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Source Annual Symposium on Computational Geometry archive
Proceedings of the nineteenth annual symposium on Computational geometry table of contents
San Diego, California, USA
SESSION: Applications table of contents
Pages: 58 - 67  
Year of Publication: 2003
ISBN:1-58113-663-3
Authors
Mirela Tanase  Institute of Information & Computing Sciences, TB Utrecht, The Netherlands
Remco C. Veltkamp  Institute of Information & Computing Sciences, TB Utrecht, The Netherlands
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 11,   Downloads (12 Months): 92,   Citation Count: 7
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ABSTRACT

We propose a novel type of decomposition for polygonal shapes. It is thought that, for the task of object recognition, the human visual system uses a part-based representation. Decompositions based on skeletons have been previously proposed in computer vision. Our method is the first one, however, based on the straight line skeleton. Compared to the medial axis, the straight line skeleton has a few advantages: it contains only straight segments and has a lower combinatorial complexity. The skeletal nodes and the way they are generated are the basis for our decomposition, which has two stages that result in a decomposition into (possibly overlapping) parts. First, a number of visually striking parts are identified, then their boundaries are successively simplified, by locally removing detail. Our method runs in time ((n+r1+r22)log2 n), after the skeleton construction, where n is the number of vertices in the polygon, r1 the number of split events, and r2 the number of reflex edge annihilations. The decomposition is invariant to rigid motions and uniform scalings. We present results indicating that it provides plausible decompositions for a variety of shapes. This makes it attractive for partial shape matching in content-based image retrieval.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Mirela Tanase: colleagues
Remco C. Veltkamp: colleagues